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GPU-based parallel optimization implement of phase diversity
Quan, Zhang1,2,3,4; Hua, Bao1,3; Changhui, Rao1,3; Zhenming, Peng2
Volume9301
Pages930137
2014
Language英语
ISSN0277786X
DOI10.1117/12.2073135
Indexed ByEi
Subtype会议论文
AbstractPhase diversity (PD) can not only be used as wavefront sensor but also as image post processing technique. However, its computations have been perceived as being too burdensome and it is difficult to achieve its real time application on a PC platform. In this paper, we carried out parallel analysis on the algorithm and task assignments on the heterogeneous platform of CPU-GPU, and then implement parallel programing optimization on GPUs. The optimization strategies of the cost function on GPU are introduced. The process of OTF is improved to make the amount of calcuation reduced by 11% compared to the original method. In order to demonstrate the speedup of PD, two images, 128×128 pixels and 256×256 pixels in dimension, are tested on CPU platform and CPU/GPU heterogeneous platform respectively. The results show the time costs have the improvenments of 13x and 28x for the implementation of PD based on GPU in contrast with that based on CPU. © 2014 SPIE.; Phase diversity (PD) can not only be used as wavefront sensor but also as image post processing technique. However, its computations have been perceived as being too burdensome and it is difficult to achieve its real time application on a PC platform. In this paper, we carried out parallel analysis on the algorithm and task assignments on the heterogeneous platform of CPU-GPU, and then implement parallel programing optimization on GPUs. The optimization strategies of the cost function on GPU are introduced. The process of OTF is improved to make the amount of calcuation reduced by 11% compared to the original method. In order to demonstrate the speedup of PD, two images, 128×128 pixels and 256×256 pixels in dimension, are tested on CPU platform and CPU/GPU heterogeneous platform respectively. The results show the time costs have the improvenments of 13x and 28x for the implementation of PD based on GPU in contrast with that based on CPU. © 2014 SPIE.
Conference NameProceedings of SPIE: International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Conference Date2014
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Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7819
Collection自适应光学技术研究室(八室)
Corresponding AuthorChanghui, Rao
Affiliation1. Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, China
2. School Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China
3. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, China
4. University of Chinese, Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Quan, Zhang,Hua, Bao,Changhui, Rao,et al. GPU-based parallel optimization implement of phase diversity[C],2014:930137.
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